Tối ưu hóa danh mục đầu tư phân tích rủi ro tại trung quốc bài học cho việt nam

Tối ưu hóa danh mục đầu tư Phân tích rủi ro Trung Quốc Bài học cho Việt Nam Võ Hồng Đức 2020 TABLE OF CONTENTS INTRODUCTION 11 RESEARCH OBJECTIVES 13 LITERATURE REVIEW 13 3.1 MARKOWITZ’S MEAN-VARIANCE OPTIMIZATION 13 3.2 MEAN-SEMIVARIANCE OPTIMIZATION FRAMEWORK 14 3.3 RESAMPLING METHODOLOGY 16 3.4 EMPIRICAL STUDIES 17 DATA AND METHODOLOGY 25 4.1 ESTIMATES OF PORTFOLIO RISK AND RETURN 25 4.2 OPTIMIZATION 29 4.3 DATA 31 RESULTS 31 5.1 RETURN, RISK, AND RANKING 31 5.2 EFFICIENT FRONTIERS 35 CONCLUSIONS 39 LIST OF TABLES Table Annualized average daily returns, standard deviations, semi-deviations and their rankings by sectors in the whole period from 2007 to 2016 in China, in percent 33 Table Rankings among sectors using daily, weekly, and monthly data sets in the period of 2007-2016 34 Table The average Sortino ratio under four optimization frameworks 37 LIST OF FIGURES Figure The figure shows the Markowitz efficient frontier as well as meansemivariance efficient frontier in the period 2007-2016 The annualized average daily returns and annualized semideviations of ten selected sectors along with the SSE Index are presented 35 Figure The figure shows the resampled efficient frontier as well as resampled mean-semivariance efficient frontier using 2007-2016 sample The annualized average daily returns and annualized semideviations of ten selected sectors along with the SSE Index are presented 36 Figure Optimal weights under three framework: (a) traditional mean-variance, (b) mean-semivariance, and (c) resampled mean- semivariance; daily data of ten sectors in China, 2007 to 2016 38 Introduction The Chinese stock market is enormous and plays a significant role to its economic growth as well as other global markets At the beginning of 2016, Shanghai market, one of the largest equity markets in the world, hits US$3.5 trillion in market capitalization (Shanghai stock exchange 2017 Factbook) A large number of listed stocks in Shanghai Stock Exchange (SSE) are from public sector, which is large and represents the whole economy of China There are also a number of studies which demonstrate a link between the stock market development in China and economic growth (Liu & Sinclair, 2008; Wong & Zhou, 2011) In addition, the fluctuations in the Chinese stock market have increasingly affected developed stock markets, such as those in the US, the UK, Germany, and Japan (Yu, Fang, Sun, & Du, 2018) In a recent study, Fang and Bessler (2018) argued that Chinese market has a powerful impact on most stock markets in Asia since the nation’s role has improved through its economy’ strong growth as well as financial openness This market interdependence is improved partly thanks to the financial market reform in China after joining the World Trade Organization in 2001 (He, Chen, Yao, & Ou, 2015) The Chinese stock market becomes more attractive as an investment opportunity due to its convergence to the global market condition Currently, foreign investors can even approach both “A-shares” and “B-shares” on the market, where the first category was restricted to local investors only before the reforms in December 2002 Yao, Ma, and He (2014) found that not only the herding behaviour at the beginning of the last decade mostly disappeared over time, but also the A-share markets seem to become more and more rational Carpenter, Lu, and Whitelaw (2015) considered that the level of market efficiency in China converges to the US This implies that the stock pricing and portfolio construction methodology is also very similar between two markets even though their levels of risk are different The convergence to global market encourages application of finance and investment theories in China, seeking for profitable opportunities Drew, Naughton, and Veeraraghavan (2003) built a multi-factor model for Chinese stock market but found that the market factors could not separately explain the return’s fluctuation Later, Xu 11 and Zhang (2014) successfully applied the Fama-French three-factors model in the Chinese stock market and argued that the model could explain more than 93 per cent of the Chinese A-share return’s movements However, investing in an emerging market like China faces a huge systematic downside risk along with attractive returns A three-week crash of the Chinese stock market in 2015 blew away about 30 per cent of its market shares, which raised a concern about a more serious influence on the world economy than the Greek debt crisis in 2011 (Allen, 2015) A strong negative impact from the crash on several Asian markets was also noted by Fang and Bessler (2018) In addition, Yu et al (2018) argued that the large magnitude of risk from the Chinese stock market, especially through downside periods, has reduced the benefit of diversification Among the most original portfolio construction theories, Markowitz meanvariance portfolio optimization (Markowitz, 1952) is commonly used to instruct investors how they can efficiently allocate their investments Unfortunately, this famous theory is also known in literature as producing biases because inputs with massive estimation error are used This weakness puts the theory into a serious trouble since such an error is amplified by the optimization procedure Consequently, estimates for the outputs, portfolio’s optimal weights as well as risk and return from this framework have arguably become less convincing For example, when an asset’s expected return is overestimated, it will be allocated much more risk by the classical mean-variance optimization method than it should be In addition, resampling method introduced by Michaud (1989) is an approach that solves the problem Moreover, since investors not shy away from the extremely positive returns, the variance used in the classical theory leads to another bias and must be replaced by a downside risk measure, such as semivariance In this paper, we will construct the portfolio optimization in China, using classical Markowitz mean-variance framework, mean-semivariance framework, and applying Michaud resampling method to the optimization procedure This paper explores two research questions First, are the rankings of risk and returns among sectors significantly 12 changed using different risk- return measures? Second, resampling method and mean-semivariance framework actually improve the optimization procedure? Findings from this study indicate that this new framework improves the performance of the optimal portfolios, measured by Sortino ratio, and diversification Research objectives It is noted that the Chinese stock market becomes more attractive as an investment opportunity due to its convergence to the global market condition The convergence to global market encourages application of finance and investment theories in China, seeking for profitable opportunities This observation leads to following research question: • Are the rankings of risk and returns among sectors significantly changed using different risk- return measures? • Do resampling method and mean-semivariance framework actually improve the optimization procedure? In order to answer to those questions, this study is conducted to achieve the following research objectives: • Ranking risk and return of 10 sectors through mean-variance optimization framework and mean-semivariance optimization framework • A comparision between mean-variance optimization framework and meansemivariance optimization work in terms of optimal portfolio selection 3.1 Literature review Markowitz’s mean-variance optimization Mean-variance optimization, which was initially introduced by Harry Markowitz in 1952, is known as a cornerstone in portfolio selection world In 1990, Markowitz shared the Nobel Memorial Prize in Economic Sciences with William Sharpe and Merton Miller for their contribution in the financial economics theory The meanvariance optimization framework uses expected return as a measure for reward and variance as a risk measure, based on historical return, volatility, and covariance matrix The outputs of this procedure are optimal portfolios with highest return in each level of 13 risk, along with their proposed weight vectors This theory is successfully tested by a number of empirical studies such as Farrar (1962) and Perold (1984) It is also the background for the famous capital asset pricing model (Sharpe, 1964) Perold (1984) insisted that the Markowitz framework has been widely accepted as a practical method for portfolio construction process However, the classical mean-variance framework has its own limitations For example, the assumption of symmetrically and normally distributed returns Ongoing studies on downside risk measures have tried to replace variance by a more appropriate risk measures Value-at-risk (VaR) is one of the candidates which is developed to mean- VaR framework to solve the optimization problem (Campbell, Huisman, & Koedijk, 2001) Conditional Value-at-risk (CVaR) is later proposed due to the fact that VaR does not own the subadditivity property, one of criteria of a coherent risk measure Recently, Vo et al (2018) applied CVaR to seek for the optimal portfolios in the South East Asian region In addition, Powell et al (2018) constructed new metrics named EVaR and ECVaR to measure downside volatility of commodity assets in various economic periods 3.2 Mean-semivariance optimization framework Semi-variance has increasingly utilized from studies on downside risk measures (Harlow, 1991; Sortino & Price, 1994; Sortino & Van Der Meer, 1991) In general, it is an asymmetric risk measure, which quantifies the deviations below the mean or a threshold level of return Estrada (2006) provided an example of using semi-variance and semideviation as the alternatives In addition, Estrada (2004) argued semivariance is superior to variance for the following three reasons First, investors only dislike downside movement on the asset returns; they will not feel harmful with upside returns, which are also included in the measurement of the ‘variance’ As such, the semivariance fits investors’ demand in analyzing risk Second, the semi-variance is more statistically helpful than variance when return is asymmetrically distributed, which is often observed in practice Finally, semi-variance is a measure that combines variance and skewness at the same time; hence, we can use single factor models to estimate the returns 14 Mean-semivariance framework is supported by both strong background theory and empirical studies Markowitz, the father of mean-variance optimization framework, argued that semi- variance appears to generate better optimal portfolios than those based on variance framework and considered that semi-variance is “more plausible than variance as a measure of risk” (Markowitz, 1959, 1991) The mean-semivariance framework attracts academic and empirical studies Estrada (2002, 2004, 2006, 2007) constructed a series of papers discuss a number of theoretical frameworks on downside risk basis, including mean-semivariance optimization framework The author also considered that the usual beta can be substituted by ‘downside beta’ and suggested using the D-CAPM as an alternative of the CAPM The author also stated that meansemivariance framework is particularly appropriate for emerging markets (Estrada, 2004) Boasson, Boasson, and Zhou (2011) used monthly data from seven exchangetraded index funds from 2002 to 2007 to construct mean-semivariance efficient frontier and recommended its application in insurance and banking sectors In addition, PlaSantamaria and Bravo (2013) utilized daily data of Dow Jones stocks over the period 2005-2009 to prove that the mean-semivariance is empirically more suitable to reflect the downside risks than a classical mean-variance optimization With respect to technique issues, although the mean-semivariance framework gains more and more trusts from academic community, it could not be easily developed due to mathematical problems In 1993, Markowitz solved the mean-semivariance optimization by transforming into quadratic problem using simulated securities (Markowitz, Todd, Xu, & Yamane, 1993) Foo and Eng (2000) calculated these figures based on lower partial moments (LPM) constructed by Harlow and Rao (1989) Estrada (2008) suggested a heuristic approach that creates a symmetric and exogenous semicovariance matrix to solve the optimization problem This paper adopts the method introduced in Ballestero (2005), which uses Sharpe’s beta regression equation (Sharpe, 1964) connecting every asset return to the whole market A semi- variance matrix and a quadric objective function are constructed, however, heuristics are not required This technique is also used in some empirical studies on mean-semivariance framework (Boasson et al., 2011) 15 3.3 Resampling methodology Resampling methodology, which is generally considered as an enhanced meanvariance optimization from Markowitz (1952), was developed by Michaud (1998) on the basis of a simulation framework A key objective of this method is to limit the effect of input estimation errors on the optimal portfolio weights and, as such, to achieve more robust portfolios through a balanced and diversified asset allocation The key distinction separating Michaud resampling method from the original Markowitz optimization is that the resampling utilizes the data from a stochastic process rather than from a predetermined data set This requires various repeats of random sample selection based on Monte Carlo simulation methodology developed by Metropolis and Ulam (1949) On a theoretical consideration, Michaud’s resampling method shows its superior in improving performance of optimal portfolios compared to the classical meanvariance optimization Markowitz and Usmen (2006) created a simulated battle where a Bayesian player, representing classical mean-variance optimization, was in competition with Resampling player, who follows the method developed by Michaud (1998) The authors found that the Resampling player won ten out of ten times Harvey, Liechty, and Liechty (2008) added that the Resampling player will show the advantages when the return distribution is not the same as the historical distribution On a practical consideration, empirical studies also demonstrated that Michaud’s resampling method will improve performance Using US risk-free asset and 10 global stock index returns, Fletcher and Hillier (2001) suggested that resampling method provides a higher Sharpe performance of optimal portfolios than the traditional meanvariance framework Cardoso (2015) found a similar result from a number of selected individual stocks in S&P500 where non-normally distributed resampling is captured The resampling method owns two valuable features for the long-term investors: diversification and stability (Fernandes & Ornelas, 2009; Kohli, 2005) Using various asset classes from US equity to Euro government bond, Delcourt and Petitjean (2011) found that the resampled optimization will result in a more stable and diversified optimal portfolios Mansor, Baharum, and Kamil (2006) run the model for Malaysian stock market and found that the method eliminates estimation error when using daily 16 5.2 Efficient frontiers Mean-variance optimization framework versus mean-semivariance optimization framework Both mean-variance and mean-semivariance optimization frameworks are plotted using exactly the same data set as presented in Figure below For each return level, the mean-semivariance framework improves efficient frontiers by reducing semideviations Interestingly, the mean-semivariance efficient frontiers are longer toward the bottom-left However, it is noted that it is shorter toward the top- right at the same time This implies that under the mean-semivariance framework, investors are provided with a wider risk preference option than under the standard mean-variance framework Figure The figure shows the Markowitz efficient frontier as well as meansemivariance efficient frontier in the period 2007-2016 The annualized average daily returns and annualized semideviations of ten selected sectors along with the SSE Index are presented Resampled efficient frontiers The purpose of resampling method is to reduce error estimation in inputs by running multiple Monte Carlo simulations For the period of 2007-2016, the annualized 35 return range of optimal portfolios is from 4.6 per cent to approximately 14 per cent Their semideviations are from 13.2 per cent to 20.7 per cent We also run the resampling process using mean-semivariance framework The mean-semivariance framework significantly improves the optimal portfolio selection when it provides considerably higher return than the mean-variance framework at each level of risk Figure The figure shows the resampled efficient frontier as well as resampled mean-semivariance efficient frontier using 2007-2016 sample The annualized average daily returns and annualized semideviations of ten selected sectors along with the SSE Index are presented Performance comparisons We calculate Sortino ratios for each optimal portfolio to compare the results from various frameworks We use the average number of optimal portfolios which offer 5.5 per cent to 13.5 per cent in returns to maintain the comparable comparison across frameworks 36 Table The average Sortino ratio under four optimization frameworks MVO Resampled MVO Average Sortino ratio Note: 33.1 24.9 MSO Resampled MSO 33.8 25.1 Mean-variance optimization (MVO); Resampled mean-variance optimization (Resampled MVO); Mean- semivariance optimization (MSO); Resampled meansemivariance optimization (Resampled MSO) Table presents findings to confirm that the mean-semivariance optimization (MSO) brings the highest average Sortino ratio, which implies the improvement of this framework from the classical framework We also note that resampling method decreases the performance of optimization procedures under both MVO and MSO For example, the average Sortino ratio of mean-variance optimization decreases from 33.1 to 24.9 after replacing historical inputs by resampling method’s inputs Diversifications Figure presents that mean-semivariance efficient frontiers provide better diversification than under the classical mean-variance optimization, especially at the lower return levels For example, three to four sectors are added in the optimal portfolios using the MVO framework while the number is up to six sectors when the MSO framework is considered In general, Healthcare sector still contributes the largest weight to the optimal portfolio at most of the risk levels observed In addition, the resampling method provides notably more diversification than the original method in both MVO and MSO frameworks Detailed percentage contribution of each sector to the optimal portfolios under various frameworks will be provided upon request 37 Figure Optimal weights under three framework: (a) traditional mean-variance, (b) mean-semivariance, and (c) resampled mean- semivariance; daily data of ten sectors in China, 2007 to 2016 38 Conclusions This paper examines the risk, return and portfolio optimization at the industry level in China over the period 2007-2016 Findings from this study indicate that Healthcare sector is the best sector in terms of risk and return among ten industries in China This observation implies that the sector was attractive in the past and needs more attention in the future We find that there is a significant change in risk rankings among the sectors when semideviation is utilised In this paper, a simple index, the Difference Index, is utilised to capture this movement in ranking Our findings indicate that a sample using monthly data appears to be mostly affected by the change in the ranking of risks using different risk measures This paper also constructs mean-semivariance optimization framework for China stock market at the industry level based on the classical Markowitz mean-variance framework Findings from this study indicate that this new framework improves the performance of the optimal portfolios, measured by Sortino ratio, and diversification As a robustness check, a simulation technique using resampling method is also utilised While it appears that the resampling method doest not appear to improve the performance of optimal portfolio, there is a remarkable advance in diversification of the optimal portfolio 39 APPENDICES Sector Annual return (%) Rank by return Rank by standard deviation Annual semideviation (%) Rank by semideviation (3) Annual standard deviation (%) (4) 46.2 40.0 42.2 (1) Basic Materials Consumer Goods Consumer Services (2) 21.5 23.9 17.9 (5) (6) 34.5 30.3 32.2 (7) Financials Healthcare Industrials Oil&Gas Technology 1.4 30.6 12.9 6.8 17.3 10 40.6 36.6 40.4 45.1 41.7 29.4 27.4 30.6 31.7 31.3 Telecom Utilities Shanghai index* 14.9 17.9 6.3 50.9 41.5 37.4 10 36.4 31.4 28.0 10 Shanghai index is not included in the ranking group on purpose and be presented as a reference benchmark Appendix 1: This table describes the annual returns, standard deviations, semideviations and their ranking by sectors and Shanghai index in the subperiod 2007-2009 There are 754 daily observations in each sector in the period Sector Annual return (%) Rank by return Annual standard deviation (%) Rank by standard deviation Annual semideviation (%) Rank by semideviation (1) Basic Materials (2) -10.6 (3) 10 (4) 30.0 (5) (6) 22.3 (7) Consumer Goods Consumer Services 1.9 -1.4 25.1 29.2 18.9 22.0 Financials Healthcare -1.2 6.8 24.6 25.7 17.6 19.0 Industrials -0.2 27.6 20.7 Oil&Gas Technology -8.5 1.8 24.4 33.0 17.7 24.7 10 Telecom -0.9 35.2 10 24.1 Utilities Shanghai index* -1.4 -0.6 25.1 23.2 18.7 17.6 Appendix 2: This table describes the annual returns, standard deviations, semideviations and their rankings by sectors and Shanghai index in the subperiod 2010-2016 There are 1,729 daily observations in each sector in the period 40 REFERENCE Allen, K (2015, Jul 2015) Why is China's stock market in crisis? 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The certainty equivalent program was obtained through a combination of a GP and recourse approach The model results were illustrated through a case study using data from S&P100 securities By taking... such as integer programming method (Bonami, 2009), goal programming method (Pendaraki, 2005), lexicographic goal programming approach (Sharma, 2006) Some meta-heuristics based approaches are also